https://github.com/neemiasbsilva/regression-in-cnns-applied-to-plant-leaf-count

Regression in Convolutional Neural Network applied to Plant Leaf Count
https://github.com/neemiasbsilva/regression-in-cnns-applied-to-plant-leaf-count

cnns computer-vision convolutional-neural-networks count cvppp data-engineering data-science dataset deep-learning deep-learning-api deep-neural-networks inception-resnet-v2 nasnet-models plant-leaf-counting plant-phenotypes plant-phenotyping regression resnet-50 tensorflow xception-model

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Regression in Convolutional Neural Network applied to Plant Leaf Count

• Host: GitHub
• URL: https://github.com/neemiasbsilva/regression-in-cnns-applied-to-plant-leaf-count
• Owner: neemiasbsilva
• License: mit
• Created: 2019-08-29T04:09:17.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2022-09-06T12:59:23.000Z (over 2 years ago)
• Last Synced: 2023-10-20T22:00:59.623Z (about 1 year ago)
• Topics: cnns, computer-vision, convolutional-neural-networks, count, cvppp, data-engineering, data-science, dataset, deep-learning, deep-learning-api, deep-neural-networks, inception-resnet-v2, nasnet-models, plant-leaf-counting, plant-phenotypes, plant-phenotyping, regression, resnet-50, tensorflow, xception-model
• Language: Jupyter Notebook
• Homepage:
• Size: 51 MB
• Stars: 17
• Watchers: 2
• Forks: 7
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Regression in Convolutional Neural Network Applied to Plant Leaf Count

> [Silva and Gonçalvez, 2019][1]

![Keras](https://img.shields.io/badge/Keras-%23D00000.svg?style=for-the-badge&logo=Keras&logoColor=white)

![TensorFlow](https://img.shields.io/badge/TensorFlow-%23FF6F00.svg?style=for-the-badge&logo=TensorFlow&logoColor=white)

## Table of Contents

- [Dataset](##dataset)

- [Experiments](##experiments)

- [Getting Started](##gettingstarted)

- [Some Results](##someresults)

- [License](##license)

- [Acknowledgement](##acknowledgement)

- [Reference](##reference)

## Dataset









 Example of the dataset used for these experiments. For more details about the dataset you can visite the CVPP web site.


This repository contains some of experiments used for evaluating the regression with cnn (__Convolutional Neural Networks__) for counting better leafs. The dataset was available of the contents __Computer Vision Problems in Plant Phenotyping__.

As result one paper was produced and publicated in the WorkShop of Computer Vision (2019) where you can ready in the following link([da Silva and Gonçalvez (2019)](https://doi.org/10.5753/wvc.2019.7627)).

__Note:__ this paper was writer 80% in Portuguese, so is crucial you understand this language.

## Experiments

**Bellow is a description about the experiments:**

 

    
 Python script and Jupyter notebook;

    

 Was used a desktop computer with

Intel(R) Xeon(R) CPU [email protected] GHz, 64 GB memory, and NVIDIA

Titan V graphics card (5120 Compute Unified Device Architecture -

CUDA cores and 12 GB graphics memory). The methods were implemented

using Keras-Tensorflow on the Ubuntu 18.04 operating

system.





## Getting Started

### Install the following packages

  * Sklearn 0.22.2.

  ```

    $ pip install -U scikit-learn

  ```

  * TensorFlow

  ```

    $ pip install tensorflow

  ```

  * Keras

  ```

    $ pip install keras

  ```

  * Matplotlib

  ```

    $ python -m pip install -U matplotlib

  ```

  * Os

  ```

    $ pip install os-sys

  ```

  * Skimage

  ```

    $ pip install scikit-image

  ```

### Run

> For run the experiment you need to access the respective architecture folder first, you can choose bellow the best way for run.

If you need run jupyter:

```

    jupyter-notebook name_experiment.ipynb 

```

You can run using python script also:

```

    python name_experiment.py

```

## Some Results

Bellow, follow two results that have been applied to the test set using the architecture __Xception__, and the metrics used was __MAE__ (Mean Absolute error) and __R2__ (Coefficient of Determination):

| __Architecture__ | __Mean Squared Error__| __Mean Absolute Error__ | __Coefficient of Determination__|

|:-------------:|:-------------:|:-------------:|:-------------:|

|__Xception__ | 1.09 | 0.46     | 0.96|

## Acknowledgement

Thanks for the teacher PhD Wesley Nunes Gonçalvez for contributing for this project.


## License

[![License](http://img.shields.io/:license-mit-blue.svg?style=flat-square)](http://badges.mit-license.org)

- **[MIT license](http://opensource.org/licenses/mit-license.php)**

## References

[1]:da Silva, Neemias Bucéli, and Wesley Nunes Gonçalves. "Regression in Convolutional Neural Networks applied to Plant Leaf Counting." Anais do XV Workshop de Visão Computacional. SBC, 2019.

#

Sincerely  Neemias B. Silva.